Frame-level descriptor.
Given an input video, we sample 5 frames per second (5 fps) to extract the CNN features. We explore various pre-trained CNN models, including AlexNet [1]}, VGG [2]} and ResNet-50 [3]}. We adopt the output from the last convolutional layer (after ReLU) of these models as the frame descriptors. The CNN feature maps are down-sampled to \(4\times 4\)  with linear interpolation to fit the TCG (we set \({\bf S=}4\times 4\)  in TCG for computational efficiency). For the epitome model, the feature maps are sampled to \(8\times 8\)  to fit the window size \({\bf W}\) . In addition, we also average all the frame descriptors over the video (sum-aggregation), as the baseline to evaluate our framework.
